#(1)支持向量機(jī)(SVM):

library(kernlab)

irismodel <- ksvm(Species ~ ., data = iris,? ?? ?? ?? ???

? ?? ?? ?? ?? ?? ?? ?type = "C-bsvc", kernel = "rbfdot",? ?? ?? ?? ?? ?? ???

? ?? ?? ?? ?? ?? ?? ?kpar = list(sigma = 0.1), C = 10,? ?? ?? ?? ?? ?? ???

? ?? ?? ?? ?? ?? ?? ?prob.model = TRUE) 

irismodel

predict(irismodel, iris[c(3, 10, 56, 68, 107, 120), -5], type = "probabilities")



#Ksvm支持自定義核函數(shù)。如

k <- function(x, y) { (sum(x * y) + 1) * exp(0.001 * sum((x - y)^2)) }

class(k) <- "kernel"

data("promotergene")

gene <- ksvm(Class ~ ., data = promotergene, kernel = k, C = 10, cross = 5)#訓(xùn)練

gene



#對(duì)于二分類問(wèn)題，可以對(duì)結(jié)果用plot()進(jìn)行可視化。例子如下

x <- rbind(matrix(rnorm(120), , 2), matrix(rnorm(120, mean = 3), , 2))

y <- matrix(c(rep(1, 60), rep(-1, 60)))

svp <- ksvm(x, y, type = "C-svc", kernel = "rbfdot", kpar = list(sigma = 2))

plot(svp)





library(e1071)

set.seed(1234)

ind<-sample(2,nrow(iris),replace=TRUE,prob=c(0.7,0.3)) #70%為訓(xùn)練集 30%為測(cè)試集

train<-iris[ind==1,]

test<-iris[ind==2,]

svm<-svm(train[,1:4],train[,5],type="C-classification",

? ?? ?? ?cost=10,kernel="radial",probability=TRUE,scale=FALSE)

pred<-predict(svm,test[,1:4],decision.values=TRUE)

table(pred,test[,5])



library(e1071)

model <- svm(Species ~ ., data = iris,? ?? ?? ?? ?

? ?? ?? ?? ?? ?method = "C-classification", kernel = "radial",? ?? ?? ?? ???

? ?? ?? ?? ?? ?cost = 10, gamma = 0.1)

summary(model)

plot(model, iris, Petal.Width ~

? ?? ? Petal.Length, slice = list(Sepal.Width = 3,? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?

? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ? Sepal.Length = 4))

pre=predict(model, iris,type='class') 

table(pre,iris$Species) 



library("klaR")

data("B3")

Bmod <- svmlight(PHASEN ~ ., data = B3,? ?? ?? ?? ?? ?? ?

? ?? ?? ?? ?? ?? ? svm.options = "-c 10 -t 2 -g 0.1 -v 0")

predict(Bmod, B3[c(4, 9, 30, 60, 80, 120),? ?? ?? ?? ???

? ?? ?? ?? ?? ?? ? -1])



#(2)支持向量回歸(SVR): 

library(DMwR) 

library(nnet) 

data(algae) 

algae <- algae[-manyNAs(algae), ] 

clean.algae <- knnImputation(algae[,1:12],k=10) 

norm.data <- scale(clean.algae[,4:12]) #數(shù)據(jù)標(biāo)準(zhǔn)化 



library(e1071) 

model.svm <- svm(a1~., norm.data) 

preds <- predict(model.svm, norm.data) 

plot(preds~ scale(clean.algae$a1)) 



library(rminer)

set.seed(1234)

svr<-fit(a1~., norm.data, model="svm") 

#利用模型進(jìn)行預(yù)測(cè) 

norm.preds <- predict(svr, norm.data) 

#繪制預(yù)測(cè)值與真實(shí)值之間的散點(diǎn)圖 

plot(norm.preds~ scale(clean.algae$a1)) 

#計(jì)算相對(duì)誤差 

(nmse2 <- mean((norm.preds-scale(clean.algae$a1))^2)/ 

?mean((mean(scale(clean.algaea1))-scale(clean.algaea1))^2))